Comparison between the genders for the ages and types

Questions

  • What are the differences between the gender for the different combinations of ages and types?
  • Do we observe the same changes as globally?

Loads

Libraries and functions

Warning message in is.na(x[[i]]):
“is.na() applied to non-(list or vector) of type 'environment'”Warning message in rsqlite_fetch(res@ptr, n = n):
“Don't need to call dbFetch() for statements, only for queries”
==========================================================================
*
*  Package WGCNA 1.63 loaded.
*
*    Important note: It appears that your system supports multi-threading,
*    but it is not enabled within WGCNA in R. 
*    To allow multi-threading within WGCNA with all available cores, use 
*
*          allowWGCNAThreads()
*
*    within R. Use disableWGCNAThreads() to disable threading if necessary.
*    Alternatively, set the following environment variable on your system:
*
*          ALLOW_WGCNA_THREADS=<number_of_processors>
*
*    for example 
*
*          ALLOW_WGCNA_THREADS=4
*
*    To set the environment variable in linux bash shell, type 
*
*           export ALLOW_WGCNA_THREADS=4
*
*     before running R. Other operating systems or shells will
*     have a similar command to achieve the same aim.
*
==========================================================================


Allowing multi-threading with up to 4 threads.
[1] "preparing gene to GO mapping data..."
[1] "preparing IC data..."
[1] "preparing gene to GO mapping data..."
[1] "preparing IC data..."
[1] "preparing gene to GO mapping data..."
[1] "preparing IC data..."

Data

Stats

Wald padj < 0.05LFC > 0 (Wald padj < 0.05)LFC < 0 (Wald padj < 0.05)
M VS F (SPF, 8w)246212911171
M VS F (GF, 8w)273014191311
M VS F (SPF, 52w)345917351724
M VS F (GF, 52w)334616891657
M VS F (SPF, 104w) 290 148 142
M VS F (GF, 104w) 95 40 55

Differentially expressed genes

  M VS F (SPF, 8w)    M VS F (GF, 8w)  M VS F (SPF, 52w)   M VS F (GF, 52w) 
         0.4890333          0.4714286          0.4541775          0.4871488 
M VS F (SPF, 104w)  M VS F (GF, 104w) 
         0.3862069          0.8842105 
Warning message in pcls(G):
“initial point very close to some inequality constraints”Warning message in pcls(G):
“initial point very close to some inequality constraints”Warning message in pcls(G):
“initial point very close to some inequality constraints”Warning message in pcls(G):
“initial point very close to some inequality constraints”Warning message in pcls(G):
“initial point very close to some inequality constraints”Warning message in pcls(G):
“initial point very close to some inequality constraints”Warning message in stack.default(getgo(rownames(l$deg), "mm10", "geneSymbol")):
“non-vector elements will be ignored”Warning message in stack.default(getgo(rownames(as.data.frame(l$deg)), "mm10", "geneSymbol", :
“non-vector elements will be ignored”

Comparison of the numbers per ages

Differentially expressed genes

DEG into gene co-expression network

  • White: up-regulated
  • Black: down-regulated
M vs F 8w 52w 104w
SPF
GF

GO analysis

Biological process

Dot-plot with the most over-represented BP GO (20 most significant p-values for the different comparison)

Using term, id as id variables
Using term, id as id variables

Network based on description similarity

M vs F 8w 52w 104w
SPF
GF

M VS F (SPF, 8w)

<!DOCTYPE html>

GO Tree at "../results/dge/gender-effect/gender_type_age/go/M_VS_F_SPF_8w.png"

M VS F (GF, 8w)

<!DOCTYPE html>

GO Tree at "../results/dge/gender-effect/gender_type_age/go/M_VS_F_GF_8w.png"

M VS F (SPF, 52w)

<!DOCTYPE html>

GO Tree at "../results/dge/gender-effect/gender_type_age/go/M_VS_F_SPF_52w.png"

M VS F (GF, 52w)

<!DOCTYPE html>

GO Tree at "../results/dge/gender-effect/gender_type_age/go/M_VS_F_GF_52w.png"

M VS F (SPF, 104w)

<!DOCTYPE html>

GO Tree at "../results/dge/gender-effect/gender_type_age/go/M_VS_F_SPF_104w.png"

M VS F (GF, 104w)

<!DOCTYPE html>

GO Tree at "../results/dge/gender-effect/gender_type_age/go/M_VS_F_GF_104w.png"

Cellular components

Dot-plot with the most over-represented CC GO (20 most significant p-values for the different comparison)

Using term, id as id variables
Using term, id as id variables

Molecular functions

Dot-plot with the most over-represented MF GO (20 most significant p-values for the different comparison)

Using term, id as id variables
Using term, id as id variables

KEGG pathways

Warning message in file.rename(from = paste("mmu", cat, ".pathview.multi.png", sep = ""), :
“cannot rename file 'mmu03010.pathview.multi.png' to '../results/dge/gender-effect/gender_type_age/kegg/over_repr_kegg/mmu03010.pathview.multi.png', reason 'No such file or directory'”
Error in `$<-.data.frame`(`*tmp*`, "labels", value = c("", "", "", "", : replacement has 34 rows, data has 48
Traceback:

1. plot_kegg_pathways(gender_age_type_deg$over_represented_KEGG[, 
 .     "category"], gender_age_type_deg$fc_deg, "../results/dge/gender-effect/gender_type_age/kegg/over_repr_kegg/")
2. suppressMessages(pathview(gene.data = fc_deg, pathway.id = cat, 
 .     species = "Mus musculus", gene.idtype = "Symbol"))
3. withCallingHandlers(expr, message = function(c) invokeRestart("muffleMessage"))
4. pathview(gene.data = fc_deg, pathway.id = cat, species = "Mus musculus", 
 .     gene.idtype = "Symbol")
5. `$<-`(`*tmp*`, "labels", value = c("", "", "", "", "", "", "", 
 . "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", 
 . "", "", "", "", "", "", "", "", "", "", ""))
6. `$<-.data.frame`(`*tmp*`, "labels", value = c("", "", "", "", 
 . "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", 
 . "", "", "", "", "", "", "", "", "", "", "", "", "", ""))
7. stop(sprintf(ngettext(N, "replacement has %d row, data has %d", 
 .     "replacement has %d rows, data has %d"), N, nrows), domain = NA)

Pathway graphs available at ../results/dge/type-effect/type_gender_age/over_repr_kegg/

Pathway graphs available at ../results/dge/type-effect/type_gender_age/under_repr_kegg/